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    Coursera
    1. Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization
    2. Week 1
    3. Practical aspects of Deep Learning
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        Quiz: Practical aspects of Deep Learning
        10 questions
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    Practical aspects of Deep Learning

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    Practical aspects of Deep Learning
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    Practical aspects of Deep Learning

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    1.
    Question 1

    If you have 10,000,000 examples, how would you split the train/dev/test set?

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    2.
    Question 2

    The dev and test set should:

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    3.
    Question 3

    If your Neural Network model seems to have high bias, what of the following would be promising things to try? (Check all that apply.)

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    4.
    Question 4

    You are working on an automated check-out kiosk for a supermarket, and are building a classifier for apples, bananas and oranges. Suppose your classifier obtains a training set error of 0.5%, and a dev set error of 7%. Which of the following are promising things to try to improve your classifier? (Check all that apply.)

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    5.
    Question 5

    What is weight decay?

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    6.
    Question 6

    What happens when you increase the regularization hyperparameter lambda?

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    7.
    Question 7

    With the inverted dropout technique, at test time:

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    8.
    Question 8

    Increasing the parameter keep_prob from (say) 0.5 to 0.6 will likely cause the following: (Check the two that apply)

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    9.
    Question 9

    Which of these techniques are useful for reducing variance (reducing overfitting)? (Check all that apply.)

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    10.
    Question 10

    Why do we normalize the inputs xxx?

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